However, the use of these external supports is limited by factors such as poor appearance, bulkiness, physical constraint,
skin irritation, and
muscle atrophy that could lead to low acceptance and compliance.
However,
patient compliance with the prescribed intervention exercises present a challenge, especially patients who are not self-motivated may not continue with the prescribed exercise programs.
There are many drawbacks regarding to this design.
First, it is intrusive.
This requirement largely affects comfort and compliance of the system, and even causes side effects such as infection.
This imposes difficulty in modifying the feedback
algorithm once the device is setup.
More importantly, it has intrinsic inability to support
adaptation of the feedback logic based either on the historical information such as patient's progress, or on external information such as doctor / specialists' opinion.
Compared to
wireless setup, wired design is less flexible, and less comfortable for the patient.
The apparatus sets criteria, which, if not met, may result in a negative reinforcement, such as unpleasant audio tone or, if the criteria are met, will reward the subject.
Even though this design considered the aspect of
adaptation, the
adaptation method it used is very primitive—it is achieved by adjusting criteria upwards or downwards.
In applications, however, the criteria are hard to set because multiple
metrics (resulting to multitude of criteria) should be considered, let alone each criterion should vary from patient to patient.
Hence, simply using criterion-based detection in this
scenario is not sufficient.
Another drawback of this design is that it proposed a tension-based sensor to detect the posture of the patients.
Compared to a modern motion sensor, which utilizes
accelerometer and
gyroscope, the tension-based sensor lacks precision, flexibility, and is prone to error (due to the strict placement requirement).
On this aspect, this proposed system is not preferable for a lack of effective means to stimulate and facilitate the patient in achieving an improvement in the posture in a progressive manner as a treatment for AIS.
As mentioned before, the naive feedback mechanism imposes difficulty in modifying the feedback
algorithm once the device is set-up.
More importantly, it has intrinsic inability to support adaptable feedback logic.
While effectiveness of these methods is largely dependent on the application area and the positioning of sensory devices, the accuracy of a reading cannot always be maintained on an acceptable confidence level.
Therefore, to be able to adopt these methods, a more sophisticated design is applied, leading to a poor appearance, bulkiness, and one or more physical constraints in a final design, all of which would in turn affect effectiveness and compliance of the devices.
However, providing an efficient detection mechanism that fully utilizes such sensor readings is still a challenging issue.
Especially in the area of
posture correction, it is impossible to define an absolutely correct posture out of the measurement provided by the sensors.
In this case, the naive feedback
algorithm with a threshold-based detection algorithm that most existing works have proposed would not suffice.
Very limited feedback means have been adopted in existing techniques.